@inproceedings{27a051581c454f56a586b479205704db,
title = "DeepOrigin: End-To-End Deep Learning for Detection of New Malware Families",
abstract = "In this paper, we present a novel method of differentiating known from previously unseen malware families. We utilize transfer learning by learning compact file representations that are used for a new classification task between previously seen malware families and novel ones. The learned file representations are composed of static and dynamic features of malware files and are invariant to small modifications that do not change the malware functionality. Using an extensive dataset that consists of thousands of variants of malicious files, we were able to achieve 97.7% accuracy when classifying between seen and unseen malware families. Our method provides an important focalizing tool for cybersecurity researchers and greatly improves the overall ability to adapt to the fast-moving pace of the current threat landscape.",
author = "Ilay Cordonsky and Ishai Rosenberg and Guillaume Sicard and David, {Eli Omid}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",
year = "2018",
month = oct,
day = "10",
doi = "10.1109/IJCNN.2018.8489667",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",
address = "United States",
}